AttentiveLearn: Personalized Post-Lecture Support for Gaze-Aware Immersive Learning

TL;DR

AttentiveLearn is a learning ecosystem that generates personalized quizzes on a mobile learning assistant based on learners' attention distribution inferred using eye-tracking in VR lectures, to provide personalized post-lecture support for immersive learning systems.

Abstract

Immersive learning environments such as virtual classrooms in Virtual Reality (VR) offer learners unique learning experiences, yet providing effective learner support remains a challenge. While prior HCI research has explored in-lecture support for immersive learning, little research has been conducted to provide post-lecture support, despite being critical for sustained motivation, engagement, and learning outcomes. To address this, we present AttentiveLearn, a learning ecosystem that generates personalized quizzes on a mobile learning assistant based on learners' attention distribution inferred using eye-tracking in VR lectures. We evaluated the system in a four-week field study with 36 university students attending lectures on Bayesian data analysis. AttentiveLearn improved learners' reported motivation and engagement, without conclusive evidence of learning gains. Meanwhile, anecdotal evidence suggested improvements in attention for certain participants over time. Based on our findings of the field study, we provide empirical insights and design implications for personalized post-lecture support for immersive learning systems.

Figures (10)

• Figure 1: An immersive virtual classroom setup. Left: students using VR headsets in a real-world environment. Right: corresponding classroom scene showing multiple perspectives of the lecture space with avatars and slides.
• Figure 2: Attention-aware personalization pipeline. Eye-tracking data from VR lectures are processed into attention metrics, which are then used to generate personalized LectureQuiz questions targeting low-attention sections.
• Figure 3: Integrating personalized support in the mobile assistant. Left: a student doing LectureQuiz after a VR lecture. Right: screenshots of the assistant, including personalized LectureQuiz, OpenChat for Q&A support, and ChatQuiz for additional practice.
• Figure 4: Study procedure over four weeks. Weeks 1–3 included a VR lecture, LectureQuiz and survey. Followed by out-of-class OpenChat for Q&A, ChatQuiz, weekly surveys, and a mini-exam. Week 4 concluded with a final exam and semi-structured interviews.
• Figure 5: (a) In-lecture focused percentage over time (minutes) across three weeks for attentive and non-attentive groups; (b) Attention Distribution Index (ADI) across lecture sections for both groups.